Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization-Reference-Cited by-同舟云学术

Using Deep Learning to Improve Ensemble Smoother: Applications to Subsurface Characterization

Published:2020-12 Issue:12 Volume:56 Page:
ISSN:0043-1397
Container-title:Water Resources Research
language:en
Short-container-title:Water Resour. Res.

Author:

Zhang Jiangjiang¹^ORCID,Zheng Qiang²,Wu Laosheng³^ORCID,Zeng Lingzao²^ORCID

Affiliation:

1. Yangtze Institute for Conservation and Development Hohai University Nanjing China

2. Zhejiang Provincial Key Laboratory of Agricultural Resources and Environment, Institute of Soil and Water Resources and Environmental Science, College of Environmental and Resource Sciences Zhejiang University Hangzhou China

3. Department of Environmental Sciences University of California Riverside CA USA

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

American Geophysical Union (AGU)

Subject

Water Science and Technology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1029/2020WR027399

Reference71 articles.

1. The Ensemble Kalman Filter in Reservoir Engineering--a Review

2. A Local Least Squares Framework for Ensemble Filtering

3. On the complexity of model complexity: Viewpoints across the geosciences

4. Toward a nonlinear ensemble filter for high-dimensional systems

5. Towards a robust parameterization for conditioning facies models using deep variational autoencoders and ensemble smoother

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